AI role · Open offer

Senior RAG Engineer — Spaces & inference

Hugging Face

Permanent (CDI)
Full-time
Senior
Inference & retrieval
Hybrid
20 Rue de la Paix, Paris, IDF 75002, France · Hybrid
Paris, France
EUR 70k–95k/year + BSPCE

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

4 skills3 tools

At a glance

How you'll work, where, and key listing dates.

Employment: Permanent (CDI), Full-time
Workplace: Hybrid
Location: 20 Rue de la Paix, Paris, IDF 75002, France · Hybrid
Compensation: EUR 70k–95k/year + BSPCE
Experience level: Senior
Team: Inference & retrieval
Benefits & perks

About this role

What the team is looking for and how this position fits.

Hugging Face is hiring a Senior Senior RAG Engineer — Spaces & inference on the Inference & retrieval team (Hybrid · Paris, France). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Hugging Face héberge l'écosystème open-source ML le plus utilisé au monde — équipe produit à Paris.

Senior RAG Engineer: retrieval, Spaces, et endpoints inference avec latence et qualité mesurées.

Missions

Améliorer les pipelines RAG (chunking, embeddings, hybrid search).
Publier des patterns réutilisables pour la communauté et l'enterprise.

Stack & environment

Python, Transformers, vector DBs, Docker, Kubernetes.

What we look for

RAG ou search en prod; culture open-source.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
PyTorch

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

Vector DBsTool
Embedding storage, hybrid search, and freshness strategies for RAG and semantic retrieval.
KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: RAG or retrieval feature with latency and quality metrics.
Several of these show up in recent shipped work—not only on your CV: Python, LangChain, PyTorch, NLP, and Vector DBs.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Paris, France)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: EUR 70k–95k/year + BSPCE, benefits in At a glance) and your expectations align.

Apply

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What the employer asked for

RAG or retrieval feature with latency and quality metrics.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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